The page below lists the coming and past seminars, and provides a link to the presentations that you may have missed. Click on a presentation title for the abstract.
Alert emails are sent to the TAU team and to the announcement mailing-list tau-seminars at inria.fr, to which anyone can subscribe by clicking here.
Some of these presentations are organized with the GT Deep Net; to subscribe to the related announcement mailing-list, click there.
From September 2018 the seminars will take place on Thursday morning at 11:11 in room 2014 (building 660), unless specified otherwise.
The presentations are recorded and available here.
- Wednesday, 17th of October, 14h30 (Shannon amphitheatre): Pan Zhang (Institute of Theoretical Physics, Chinese Academy of Sciences): Solving Statistical Mechanics using Variational Autoregressive Networks
- Friday, 5th of October, 11h30 (usual room R2014): Thomas Lucas (Toth team, INRIA Grenoble): Mixed batches and symmetric discriminators for GAN training
- Thursday, 6th of September, 14h30 (usual room R2014): Mo Yang (TAU/CDS-LAL)'s end of internship: Prediction of storm trajectories
- Friday, 29th of June, 16h (Shannon amphitheatre): Thomas Schmitt (TAU)'s PhD defense: Appariements Collaboratifs des Offres et Demandes d'Emploi
- Thursday, 28th of June, 14h30 (Shannon amphitheatre): Alexandre Aussem (LIRIS - Lyon): Identifying irreducible disjoint factors in multivariate probability distributions: Application to multilabel learning
- Friday, 22nd of June, 11h (Shannon amphitheatre): Peter Bosman (CWI, Delft): Gene-pool Optimal Mixing Evolutionary Algorithms - From Foundations to Applications
- Friday, 22nd of June, 8h30 - 17h (room 1046): Isabelle Guyon's group seminar day: MEDI-CHAL / L2RPN
- June, Tuesday 12th (Shannon amphitheatre): Bérénice Huquet, Amandine Pierrot, Georges Hébrail (EDF Lab Paris-Saclay): Non-Intrusive Load Monitoring (NILM) problems and studies at EDF R&D
- June, Thursday 7th (17h): PhD seminar: Zhengying Liu: No Free Lunch Theorems
- June, Tuesday 5th: Martin Toth (TAU/CentraleSupelec): Deep Learning for skin disease diagnosis assistance
- May, Thursday 31st (Shannon Amphitheatre, 14h30): François Gonard's PhD defense: Cold-start recommendation: from algorithm portfolios to job applicant matching
- May, Wednesday 30th: Diviyan Kalainathan: Tutorial on Docker
- May, Tuesday 29th: Yufei Han (Symantec Research labs): Multi-label Learning with Highly Incomplete Data via Collaborative Embedding
- May, Thursday 24th: Stuart Russell (UC Berkeley): Provably Beneficial Artificial Intelligence, at the DATAIA Institute (Turing building, 11am)
- May, Monday 7th: Jean-Noël Vittaut (Paris 8): General Game Playing pour les jeux à information parfaite ou imparfaite
- May, Friday 4th, 11h: Dominique Fourer (IRCAM): Analysis of non-stationary and multicomponent signals with applications to music information retrieval
- April, Wednesday 25th: Joon Kwon (CMAP): Mirror descent strategies for regret minimization and approachability
- April, Tuesday 17th: Bertrand Thirion (Parietal team, Neurospin, INRIA/CEA): Statistical inference for high-dimensional data & application to brain imaging
- April, Tuesday 10th: Berna Bakir Batu (TAU team): A Reinforcement Learning Approach for Simulating Cascading Failures in Power Grids
- April, Tuesday 3rd: Benjamin Donnot (TAU team): Fast Power system security analysis with Guided Dropout
- March, Tuesday 27th: Nizam Makdoud (TAU team): Intrinsic Motivation, Exploration and Deep Reinforcement Learning
- March, Tuesday 20th: Hugo Richard (Parietal/TAU teams, INRIA): Data based analysis of visual cortex using deep features of videos (more information...)
- March, Tuesday 13th: David Rousseau (Laboratoire de l'Accélérateur Linéaire (LAL), Orsay): TrackML : The High Energy Physics Tracking Challenge (more information...)
- March, Tuesday 6th: Ulisse Ferrari (Institut de la Vision): Neuroscience & big-data: Collective behavior in neuronal ensembles (more information...)
- March, Friday 2nd: François Landes (IPhT): Physicists using and playing with Machine Learning tools: two examples (more information...)
- February, Tuesday 27th: Wendy Mackay (INRIA/LRI ExSitu team): Human-Computer Partnerships: Leveraging machine learning to empower human users (more information...)
- February, Tuesday 20th: Jérémie Sublime (ISEP): Unsupervised learning for multi-source applications and satellite image processing (more information...)
- February, Friday 16th: Rémi Leblond (INRIA Sierra team): SeaRNN: training RNNs with global-local losses (more information...)
- February, Tuesday 13th: Zoltan Szabo (CMAP & DSI, École Polytechnique): Linear-time Divergence Measures with Applications in Hypothesis Testing (more information...)
- January, Tuesday 23rd (usual room 2014): Olivier Goudet & Diviyan Kalainathan (TAU): End-to-end Causal Generative Neural Networks (more information...)
- January, Friday 19th, whole day (IHES): workshop stats maths/info du plateau de Saclay (more information...)
- January, Tuesday 9th (room 435, "salle des thèses", building 650): Michèle Sébag & Marc Schoenauer (TAU): Stochastic Gradient Descent: Going As Fast As Possible But Not Faster (more information...)
- December, Tuesday 19th, 14:30 (room 455, building 650): Antonio Vergari (LACAM, University of Bari 'Aldo Moro', Italy): Learning and Exploiting Deep Tractable Probabilistic Models (more information...)
- December, Wednesday 13th, 14:30 (room 445, building 650): Robin Girard (Mines ParisTech Sophia-Antipolis): Data mining and optimisation challenges for the energy transition (more information...)
- December, first week: break (NIPS)
- November, Wednesday 22th, 14:30 (room 2014): Marylou Gabrié (ENS Paris, Laboratoire de Physique Statistique): Mean-Field Framework for Unsupervised Learning with Boltzmann Machines (more information...)
- November, Friday 17th, 11:00 (Shannon amphitheatre): [ GT DeepNet ] Levent Sagun (IPHT Saclay): Over-Parametrization in Deep Learning (more information...)
- November, Wednesday 15th, 14:30 (room 2014): Diviyan Kalainathan & Olivier Goudet (TAU): Causal Generative Neural Networks (more information...)
- November, Thursday 9th, 11:00 (Shannon amphitheatre): Claire Monteleoni (CNRS-LAL / George Washington University): Machine Learning Algorithms for Climate Informatics, Sustainability, and Social Good (more information...)
- October, Tuesday 24th, 14:30 (Shannon amphitheatre): Benjamin Guedj (MODAL team, Inria Lille): A quasi-Bayesian perspective to NMF: theory and applications (more information...)
- October, Wednesday 18th, 14:30 (room 2014): Théophile Sanchez (TAU): End-to-end Deep Learning Approach for Demographic History Inference (more information...)
- October, Wednesday 11th, 14:00 (room 2014): Victor Estrade (TAU): Robust Deep Learning : A case study (more information...)
- October, Wednesday 4th, 14:30 (room 2014): Hugo Richard (Parietal/TAU): Data based alignment of brain fmri images (more information...)
- September, Tuesday 19th, 11:00 (Shannon amphitheatre): Carlo Lucibello (Politecnico di Torino): Probing the energy landscape of Artificial Neural Networks (more information...)
- July, Tuesday 4th, from 11:00 to 13:00 (Shannon amphitheatre): presentation of Brice Bathellier's team + MLspike by Thomas Deneux (more information...)
- June, Friday 30th, 14:30 (room 2014): internships presentation by Giancarlo Fissore: Learning dynamics of Restricted Boltzmann Machines, and by Clément Leroy: Free Energy Landscape in a Restricted Boltzmann Machine (RBM) (more information...)
- June, Thursday 29th, 14:30 (Shannon amphitheatre): [ GT DeepNet ] Alexandre Barachant: Information Geometry: A framework for manipulation and classification of neural time series (more information...)
- June, Tuesday 27th, 14:30 (room 2014) Réda Alami et Raphaël Féraud (Orange Labs): Memory Bandits : A bayesian Approach for the Switching Bandit Problem (more information...)
- June, Monday 12th, 14:30 (Shannon amphitheatre): [ GT DeepNet ] Romain Couillet (Centrale-Supélec): A Random Matrix Framework for BigData Machine Learning (more information...)
- May, Wednesday 24th, 16:00 (room 2014): Priyanka Mandikal (TAU): Anatomy Localization in Medical Images using Neural Networks (more information...)
- April, Friday 28th, 14:30 (Shannon amphitheatre): [ GT DeepNet ] Jascha Sohl-dickstein (Google Brain): Deep Unsupervised Learning using Nonequilibrium Thermodynamics (more information...)
- April, Tuesday 3rd: Thomas Schmitt: RecSys challenge 2017 (more information...)
- March, Thursday 2nd, 14:30 (Shannon amphitheatre): Marta Soare (Aalto University): Sequential Decision Making in Linear Bandit Setting (more information...)
- February 22nd, 11h: Enrico Camporeale (CWI): Machine learning for Space-Weather forecasting
- February, Thursday 16th (Shannon amphi.), 14h30: [ GT DeepNet ] Corentin Tallec: Unbiased Online Recurrent Optimization (more information...)
- February 14th (Shannon amphi.), 14h: [ GT DeepNet ] Victor Berger (Thales Services, ThereSIS): VAE/GAN as a generative model (more information...)
- January 25th, 10h30: Romain Julliard (Muséum National d'Histoire Naturelle): 65 Millions d'Observateurs (more information...)
- January 24th: Daniela Pamplona (Biovision team, INRIA Sophia-Antipolis / TAO): Data Based Approaches in Retinal Models and Analysis (more information...)
- November 30th: Martin Riedmiller (Google DeepMind). Deep Reinforcement learning for learning machines (more information...)
- November 29th: Amaury Habrard (Universite Jean Monnet de Saint-Etienne). Domain Adaptation with Optimal Transport: Mapping Estimation and Theory (more information...)
- November 24th: [ GT DeepNet ] Rico Sennrich (University of Edinburgh). Neural Machine Translation: Breaking the Performance Plateau (more information...)
- June 28th: Lenka Zdeborova (CEA,Ipht). Solvable models of unsupervised feature learning LRI_matrix_fact.pdf
- May 3rd: Emile Contal (ENS-Cachan). The geometry of Gaussian processes and Bayesian optimization. slides_semstat16.pdf
- April 26: Marc Bellemare (Google DeepMind). Eight Years of Research with the Atari 2600 (more information...)
- April 12: Mikael Kuusela (EPFL). Shape-constrained uncertainty quantification in unfolding elementary particle spectra at the Large Hadron Collider.(more information...)
- March 22nd: Matthieu Geist (Supélec Metz): Reductions from inverse reinforcement learning to supervised learning (more information...)
- March 15: Richard Wilkinson (University of Sheffield): Using surrogate models to accelerate parameter estimation for complex simulators (more information...)
- March 1st: Pascal Germain (Université Laval, Québec): A Representation Learning Approach for Domain Adaptation (more information...)
- February 9th: François Dufour (INRIA Bordeaux) (more information...)
- January 26th: Laurent Massoulié: Models of collective inference.(more information...).
- January 19th: Sébastien Gadat: Regret bounds for Narendra-Shapiro bandit algorithms (more information...)..
- December 15th: Joon Kwon: SPARSE REGRET MINIMIZATION.(more information...).
- November 19th: Phillipe Sampaio: A derivative-free trust-funnel method for constrained nonlinear optimization (more information...).
- October 27: Audrey Durand: Bandits for healthcare (more information...).
- October 20th: Jean Lafond: Low Rank Matrix Completion with Exponential Family Noise (more information...).
- October 13th
- Sept. 28th
- Olivier Pietquin, Approximate Dynamic Programming for Two-Player Zero-Sum Markov Games OlivierPietquin_ICML15.pdf
- Francois Laviolette, Domain Adaptation (slides soon)
- July 2nd:Alaa Saade:MaCBetH : Matrix Completion with the Bethe Hessian(more information...)
- June 15th: Claire Monteleoni:Climate Informatics: Recent Advances and Challenge Problems for Machine Learning in Climate Science
- June 2nd: Robyn Francon: Reversing Operators for Semantic Backpropagation
- May 18th:Andras Gyorgy:Adaptive Monte Carlo via Bandit Allocation
- April 28th:Vianney Perchet:Optimal Sample Size in Multi-Phase Learning(more information...)
- April 27th:Hédi Soula, TBA
- April 21th: Gregory Grefenstette, INRIA Saclay: Personal semantics(more information...)
- April 7th: Paul Honeine: Relever deux défis majeurs en apprentissage par méthodes à noyaux:problème de pré-image et apprentissage en ligne (more information...)
- March 31th: Bruno Scherrer (Inria Nancy): Non-Stationary Modified Policy Iteration (more information...)
- March 24th: Christophe Schülke(ESPCI): Community detection with modularity: a statistical physics approach (more information...)
- March 10th: Balazs Kegl: Rapid Analytics and Model Prototyping (more information...)
- February 24th: Madalina Drugan (Vrije Universiteit Brussel, Belgium): Multi-objective multi-armed bandits (more information...)
- February 20th: Holger Hoos (University of British Columbia, Canada): séminaire MSR - see the slides
- February 17th :Aurélien Bellet: The Frank-Wolfe Algorithm: Recent Results and Applications to High-Dimensional Similarity Learning and Distributed Optimization more information...
- February 10th, Manuel Lopes 15interlearnteach.pdf
- January 27th :Raphaël Baillyra: Tensor factorization for multi-relational learning ((more information...)
- January 13th : Francesco Caltagirone: On convergence of Approximate Message Passing (talk_Caltagirone.pdf)
- January 6th : Emilie Kaufmann: Bayesian and frequentist strategies for sequential resource allocation (Emilie_Kauffman.pdf)
- November 4th :Joaquin Vanschoren:OpenML: Networked science in machine learning
- Oct. 28th,
- Antoine Bureau, "Bellmanian Bandit Network"
-1- Manuel Lopes, Tobias Lang, Marc Toussaint, and Pierre-Yves Oudeyer. Exploration in model-based reinforcement learning by empirically estimating learning progress. In Neural Information Processing System (NIPS), 2012.
- Basile Mayeur
Taking inspiration from inverse reinforcement learning, the proposed Direct Value Learning for Reinforcement Learning (DIVA) approach uses light priors to gener- ate inappropriate behavior’s, and use the corresponding state sequences to directly learn a value function. When the transition model is known, this value function directly defines a (nearly) optimal controller. Otherwise, the value function is extended to the (state,action) space using off-policy learning.
The experimental validation of DIVA on the Mountain car shows the robustness of the approach comparatively to SARSA, based on the assumption that the tar- get state is known. Lighter assumptions are considered in the Bicycle problem, showing the robustness of DIVA in a model-free setting.
- Thomas Schmitt, "Exploration / exploitation: a free energy-based criterion"
- Oct. 14th, Holger Hoos Slides attached.
- Sept. 29th, Rich Caruana